📚 OpenCitations Scraper

🚀 Extract citation networks and bibliographic metadata from OpenCitations in seconds. Search by DOI, PMID, or OMID. No coding, no API keys required.

🕒 Last updated: 2026-04-16 · 📊 20 fields · 🔍 Citations and references modes · 📄 Optional detailed metadata

OpenCitations is an open scholarly infrastructure providing free access to citation data from millions of academic publications. This scraper collects citation relationships, self-citation flags, and optional bibliographic metadata (authors, titles, venues, publication dates) for any publication identified by DOI, PubMed ID, or OpenCitations Meta ID. Choose between citations mode (who cited this work) and references mode (what this work cites) to map research influence in either direction.

Researchers, bibliometric analysts, and data scientists use this actor to build citation networks, track research impact, identify self-citations, and analyze how knowledge flows between publications. Instead of querying the OpenCitations API manually and parsing responses, you get clean, structured data exported as JSON, CSV, or Excel. With metadata enabled, every record includes the citing and cited entity IDs, creation date, timespan, self-citation flags, plus the full title, authors, publication date, venue, and publisher.

🎯 Target Audience	💡 Use Cases
Bibliometric analysts	Map citation networks and measure impact
Academic researchers	Track who cites your publications
University administrators	Evaluate research impact for departments
Science policy makers	Analyze knowledge flow between institutions
Data scientists	Build citation graph datasets for analysis
Librarians	Enrich catalog records with citation data

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📋 What the OpenCitations Scraper does

• 🔍 DOI-based search to find citations or references for any published work
• 🆔 PMID support for biomedical publications indexed in PubMed
• 📋 OMID support for OpenCitations internal identifier lookups
• 🔄 Bidirectional search with citations (incoming) and references (outgoing) modes
• 📊 Self-citation detection with flags for author and journal self-citations
• 📝 Optional metadata including titles, authors, venues, and publication dates

The scraper queries the OpenCitations API with your identifier and search type, retrieves all matching citation relationships, and extracts structured data for each record. When metadata is enabled, it also fetches detailed bibliographic information for each citing or cited work. Results include unique citation identifiers (OCI), entity IDs, creation dates, timespans, self-citation flags, and full publication metadata.

💡 Why it matters: Manually collecting citation data from OpenCitations involves API queries, pagination, and metadata enrichment. This scraper handles everything automatically, delivering structured citation networks ready for analysis, visualization, or integration with other research tools.

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🎬 Full Demo

🚧 Coming soon...

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⚙️ Input

Field	Type	Required	Description
maxItems	integer	No	Max records to collect. Free: up to 10. Paid: up to 1,000,000
doi	string	No	Digital Object Identifier (e.g., 10.1016/j.jmb.2005.08.075)
pmid	string	No	PubMed ID for biomedical publications
omid	string	No	OpenCitations Meta Identifier (e.g., omid:br/06140242082)
searchType	string	No	Search direction: citations (incoming) or references (outgoing)
includeMetadata	boolean	No	Fetch detailed metadata (title, authors, date) for each record

Example 1: Get citations for a DOI

{
  "doi": "10.1016/j.jmb.2005.08.075",
  "searchType": "citations",
  "includeMetadata": true,
  "maxItems": 50
}

Example 2: Get references from a PubMed article

{
  "pmid": "16325459",
  "searchType": "references",
  "includeMetadata": true,
  "maxItems": 100
}

⚠️ Good to Know: Provide one identifier (DOI, PMID, or OMID), not multiple. Enabling metadata makes the scraper slower but provides full bibliographic details for each citation. The default search type is "citations" (incoming citations).

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📊 Output

🧾 Schema

Emoji	Field	Type	Description
📝	oci	string	Unique Open Citation Identifier
👤	citing	string	Identifier of the citing entity
👤	cited	string	Identifier of the cited entity
📅	creationDate	string	When the citation relationship was recorded
⏱️	timespan	string	Time between publication dates
📊	journalSelfCitation	boolean	Whether the citation is within the same journal
📊	authorSelfCitation	boolean	Whether the author cites their own work
📝	title	string	Publication title (with metadata enabled)
👥	authors	string	Author names (with metadata enabled)
📅	publicationDate	string	Publication date (with metadata enabled)
📖	volume	string	Journal volume
📄	issue	string	Journal issue
📍	venue	string	Journal or venue name
🏷️	publicationType	string	Type of publication
📄	page	string	Page range
🏢	publisher	string	Publisher name
✏️	editor	string	Editor name
🆔	workId	string	Internal work identifier
⏰	scrapedAt	string	Collection timestamp
⚠️	error	string	Error message if processing failed

📦 Sample records

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✨ Why choose this Actor

Feature	Details
🔍 Three identifier types	Search by DOI, PubMed ID, or OpenCitations Meta ID
🔄 Bidirectional search	Find incoming citations or outgoing references
📊 Self-citation detection	Flags for author and journal self-citations
📝 Optional metadata	Full bibliographic details when enabled
🆓 Open data	All OpenCitations data is freely available
📦 Flexible export	JSON, CSV, or Excel output
⚡ Automatic pagination	Handles large citation networks automatically

📊 Map citation networks for any publication with up to 1,000,000 records per run, including self-citation detection and full metadata.

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📈 How it compares to alternatives

Feature	This Actor	Manual API Queries	Generic Scrapers
DOI, PMID, and OMID support	✅	Manual	❌
Self-citation detection	✅	✅	❌
Optional metadata enrichment	✅	Manual	❌
Bidirectional search	✅	Manual	❌
Bulk collection (1M+ records)	✅	Manual	❌
Structured JSON/CSV output	✅	JSON only	Varies
Scheduled runs	✅	❌	❌

Get structured citation data at scale without writing API code or managing pagination.

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🚀 How to use

1. Create an Apify account - Sign up free with $5 credit
2. Open the OpenCitations Scraper - Navigate to the actor page on Apify
3. Enter a DOI, PMID, or OMID - Provide the identifier for the publication you want to analyze
4. Choose search type and options - Select citations or references mode and enable metadata if needed
5. Click Start - The actor collects citation relationships and delivers structured data

⏱️ A typical run with 50 citations completes in under 1 minute.

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💼 Business use cases

📊 Bibliometric Analysis Map citation networks for research impact assessment Identify self-citations to calculate adjusted metrics Track citation accumulation over time Compare citation patterns across disciplines	🎓 Academic Research Build citation graphs for literature reviews Track who is citing your publications Identify influential papers in your field Analyze reference patterns in competitor research
🏛️ Research Administration Evaluate faculty research impact for reviews Track department-level citation metrics Monitor publication influence across programs Build reporting dashboards for stakeholders	📈 Data Science Build citation graph datasets for network analysis Train models on citation prediction tasks Analyze knowledge flow between research fields Create visualization datasets for research mapping

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🔌 Automating OpenCitations Scraper

Integrate the OpenCitations Scraper into your workflow using the Apify API or client libraries.

Node.js:

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_API_TOKEN' });
const run = await client.actor("parseforge/open-citations-scraper").call({
  doi: "10.1016/j.jmb.2005.08.075",
  searchType: "citations",
  includeMetadata: true,
  maxItems: 100
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Python:

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")
run = client.actor("parseforge/open-citations-scraper").call(run_input={
    "doi": "10.1016/j.jmb.2005.08.075",
    "searchType": "citations",
    "includeMetadata": True,
    "maxItems": 100
})
items = list(client.dataset(run["defaultDatasetId"]).iterate_items())
print(items)

• 📖 Apify API reference
• 🐍 Python client docs
• 📦 Node.js client docs

Schedules: Set up recurring runs to monitor citation growth for your publications. Configure weekly or monthly schedules from the Apify Console to track new citations automatically.

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❓ Frequently Asked Questions

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🔌 Integrate with any app

• 🔗 Make (Integromat) - Connect citation data to Google Sheets, Notion, or any of 1,500+ apps
• 🔗 Zapier - Trigger workflows when new citations are detected
• 🔗 Slack - Get notified when new citations appear for your publications
• 🔗 Airbyte - Stream citation data into your data warehouse
• 🔗 GitHub - Store citation datasets in repositories for version control
• 🔗 Google Drive - Automatically save CSV exports to shared folders

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💡 Pro Tip: Combine the OpenCitations Scraper with the Crossref Scraper to get both citation networks and full publication metadata for each cited work.

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🆘 Need Help? Open our contact form and we will get back to you within 24 hours. We are happy to help with custom setups, integrations, or feature requests.

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Disclaimer: This actor is not affiliated with, endorsed by, or connected to OpenCitations. It accesses publicly available data through the OpenCitations API. Use responsibly and in accordance with applicable terms of service.